Stop with Expertise Location…

… It’s a People Recommendation System that we need!

As the expression goes “beauty is in the eye of the beholder” and this was never more true of expertise. There is no single measurement for expertise since it completely depends on what YOU want from the so-called “expert”. If you are a sales guy looking to close a big Websphere deal, for example, then you are probably not looking for the lead developer on Websphere, but rather you are looking for someone who knows Websphere well but more importantly has had some customer exposure and ideally helped close a similar deal in the past. In this case their “deep expertise” is less important than their “customer expertise”. Now if you are a customer suppport rep and are buried knee deep in a tricky customer problem and need help… well heh! you may indeed be looking for this deep Websphere expert.

Also, a point we frequently forget is that “expert matching” also depends heavily on what THEY want from you (and/or are willing to give you). There is no point in finding someone who is completely unavailable to help you because of other priorities. And from the other side, the last thing you want is for one of your best guys to be bombarded by every Tom, Dick, and Harry in the organization. You need to spread the load across your organization by finding the people who uniquely match the particular need that you have, rather than someone who generically meets your need (ie. is a Websphere expert).

So in a nutshell, to make People Recommenders work you need to ensure a broad spectrum of features are being included in your analysis, features which align to the needs of your organization.

9 Comments to “Stop with Expertise Location…”

Great post Marie. People recommendation problem isn’t just about searching for the expertise, it is also about optimization of supply (experts) and demand (project positions, collaboration opportunities) and thus about the system wide cost of engaging the expert. The optimization must be smart enough to recognize that the best expert on topic X should be available only to highest ranking members in the organization (e.g. Directors, VPs) and the most important projects (e.g. in terms of $$$, strategic importance)

Couldn’t agree more (re: its more than just finding experts) and another reason why I hate the term “expertise location” since it narrows down the focus unnecessarily.

You have also touched on a really subtle but critical point about the “$$$ cost of engaging an expert”.

The cost consideration further reinforces the point “… finding the people who uniquely match the particular need that you have, rather than someone who generically meets your need …”. In an ideal situation you want to find the person who EXACTLY meets the needs of the business but NOT someone who EXCEEDS those requirements. That is an inefficient use of skills within the organization where that person might be better assigned to another activity within the enterprise.

Good point. What would really be cool is a tool that inputs say, my LinkedIn profile, and outputs a set of people with whom I should consider connecting. I guess LinkedIn does this in a crude way now, but how could you improve it? What it lacks is deep contextual semantic analysis of the work I’ve done. I suppose it uses LSI or some such statistical algorithm, which matches the keywords in my profile to the keywords in other profiles. This is like search 1.0. As a result, I get a lot of false positives when the tool suggests people to connect with. Perhaps if it looked more deeply at my connections and found commonality between them, it would do a better job. Thing is, there’s plenty of public data on LinkedIn alone to work with. We should be able to build something smarter.

Your point about introducing semantics is so on-the-ball. In order to really make people recommendations highly accurate for the business we need to semantically type all the relationships that are critical for the people modelling process, which in turn needs to align to the business model. So I don’t just want some LSI to tell me that Marie is connected to keywords semtech, oracle, ibm, automotive, david, and john. I specifically want to know what these terms are (topic, company, person), the type of relationship (interested_in, worked_for, works_for, friend_of, colleague_of), and the importance of certain types of relationships (is friend_of more important than colleague_of).

I agree that people recommender would be much better than expertise locator. But I think both will fail, unless the model of use changes. Martin’s reply in G+ has a good point, which has been my position for a long time: the problem can only be solved in a workspace/business that operates by “pull”, not by “push”. An approach where everyone is trying to chip off a little expertise from someone else “at no apparent cost” does not add up – closed systems like that do not function, which had been proven by economists (to start here, for example http://en.wikipedia.org/wiki/Tragedy_of_the_commons).
The reason the idea is so popular is that in very big organizations and in the web there is always this “free energy” – the free time and expertise that can be exploited with great effect. But it only works if the “energy collection” activity constitutes a reasonably small part of all operations. Trying to “industrialize” the approach will inevitably fail. Wind turbines work because they can never stop all the wind in the world, but people’s time in an organization is a finite resource. Unfortunately, the 20-th century business model is incompatible with the new technology…

I think we are all in violent agreement that the system is definitely broken, but your human perspective is a really nice way of looking at the problem from an organizational as opposed to technical angle. I guess part of what I was trying to articulate on the recommendation side of things is that better modelling and a consideration of the fact that “I need something from you PLUS you need to want to give something to me” will help some of these issues. I also like the push vs. pull point, and you have my total support on that one (I touched on that in my earlier blog post “Streams is the new Content”).

Marie. Nice to meet you! ‘expertise location’ as a concept is a tough nut for any organization to crack. From my POV, all of the above comments are valid and issues which I an my team have personally considered. In my experience in developing an enterprise wide expertise identification & enrollment system that ties into a question & answer service is tricky stuff. And to do it right, involves an ongoing commitment from the business, whatever business you’re in, to iterate and evolve – to fail quickly and not be afraid of those mistakes along the way. ‘expertise’ as a label is the broader issue with the problem you identify. Who’s an expert anyway? It’s a term that means so many things to so many people that end-user subjectivity, and in fact the subjectivity of the people that are being tapped as ‘experts’ gets in the way.

Happiness with the resources you’ve identified as an ‘answer seeker’ are likely in the eye of the beholder – did you/the system help me to get an answer to my question or help me to understand an idea – then whomever the ‘answer provider’ was makes that person the expert of the moment. As someone trying to deploy the resource to surface that expertise the question might be slightly different – was I able to identify a resource with the right set of skills/background/POV on a subject that can help me to drive a business objective? What the ‘seeker’ is looking for and the timeliness of the anticipated responses and the context thereof all drive happiness or despair.

Businesses need to define who their experts are, the thought leaders, theihelpful hands and everyone in between and apply the right validation criteria to each role. Doing so ensures that not only do the resources in the ‘expertise’ pool know what they are signing up for but, it also shows them what it takes to qualify at various levels in the ecosystem. The consumers of those resources, once codified (plopped into a database somewhere), also need to know who the resources are that are being held up as ‘experts’ to help set expectations for the anticipated interaction with that person. Public declaration/definitions might be in order depending on the company and circumstances. Subliminally, the context of the interaction (where you encounter the resource, what content they are lined up against, how you learned about that resource in the first place, etc.) will also help to refine expectations.

‘Stop with Expertise Location’ – well that’s darn near impossible. Human beings will always seek answers to questions and look for more knowledge about an area of interest. What businesses need to do is figure out just how to codify the people who can provide the insights and answers to those knowledge seekers in the proper medium (face to face, via social media, email, 800#, etc.), and to create an ecosystem that makes it all manageable. Expertise location is a necessary component to any organization that wants to become a true Social Business, and a fundamental tool supporting transparency, access, and acting nimbly.

I think what many organizations are painfully aware of is that their employees, whether they like it or not, are ‘out there’ using social computing mechanisms to share their knowledge and perspectives. Organizations therefore, must figure out how to harness that creativity/energy/knowledge, teach their employees the rules of the Social Computing road, give them permission to act as long as they adhere to that organizations business rules/rules of employment, and do so avoiding undue cybersecurity risks. ‘Expertise Location’ although a bad label, is a terrific and necessary vehicle to capitalize on their employees willingness to engage with customers, prospects and peers while building their personal and professional reputations.

Yep, this is definitely a tough nut to crack, and as yourself and Alex Nevidomsky highlighted is in essence not as much a technology question as an organizational one.

In essence I would agree with your analysis with one slight divergent point, and that is around the formalization of “the business defining who their experts are”. In my experience (and this is perhaps a bit anecdotal) I frequently find that the people who often “bubble up” as being the experts are not in fact the people who are really doing the expertise sharing (the hands-on, roll-your-sleeve-up, help-your-colleague stuff). In fact I have a simple rule that I never trust someone who claims they are an “expert”. And if someone is very visibly an expert, there is a good.chance they won’t have time to share their expertise with me. [ assuming we even agreed on what expertise means ]

In fact I might argue (and this is somewhat speculative) that experts are best found through the social network and are best identified and rated by people who have actively worked with them. When I am looking for help the first thing I do is reach out to my social network and the plus thing here is that this socially driven approach fosters reciprocity. If I look for something from my social network and have not given anything back in a long time, chances are someone is going to tell me to take a hike … at least I hope they do because I would deserve it. I guess the social component is why I am so interested and intrigued by this problem.

So all in all… a tough one … however all the best problems generally are.

I was just re-reading your post (a good read) and wanted to clarify that the title “Stop with the Expertise Location…” really just means to me that we should stop calling it expertise location and People Recommendation Systems seems like a reasonable alternative. In much the same way as I hate the term Influence, I think we all agree that Expertise is definitely an “eye of the beholder” kind of thing and a very contentious word.

People will most definitely continue to need access to experts, whatever that means!

And thanks for the great comment … its really fun to see such good dialog. It’s what innovation & growth is all about :-)